Julie Gould 00:09

Hello, and welcome to Working Scientist, a Nature Careers podcast. I’m Julie Gould. This is the third episode of the career planning series, supported by the International Science Council.

Julie Gould 00:28

There are many different ways to approach career planning, and one of them is to create a plan and follow it.

Decide where you’d like to be in two, five, ten years’ time, create some smart (SMART) goals, (which, we learned in episode two, are goals that are specific, measurable, action oriented, realistic and time bound), and then follow the steps to getting there.

People sometimes follow plans like this with laser focus, often because they’re driven by a clear passion. And staying on a fixed, focused path helps them build expertise and credibility.

In competitive fields like science, focus also increases chances of securing funding, recognition and career milestones. And for some it simply matches their personality, thriving on structure, consistency and steady progress.

In this episode, we’ll hear career stories from two people who have had this focus. They have known from early in their careers where they wanted to end up, and we’ll hear how they did it.

And as with the other episodes in this series, at the end we have a sponsored slot from the International Science Council, with the support of the China Association for Science and Technology.

The ISC is exploring how early and mid-career researchers can navigate their careers in a constantly evolving scientific landscape through conversations with emerging and established scientists.

Julie Gould 01:51

The first person we’ll hear from is Professor Sam Smith, a behavioral oncologist at the University of Leeds in the UK.

I spoke to Sam to find out more about how he followed his passion with a drive and determination similar to what I’ve seen in athletes. To push themselves to the limit, to see how far they’ll go.

He discovered his passion in the final year of his undergraduate degree in psychology, when he was offered an opportunity to understand how someone is feeling in the context of cancer,

Sam Smith 02:18

It seemed the logical thing to do, to try and push myself towards. I’ve been given this opportunity to work in an amazing research group with an amazing team. And I wanted to make the most of that opportunity, and I still do to see how far I can, I can go and push myself.

Julie Gould 02:39

When you have that kind of drive to want to do that, I imagine you then start putting goals in place, or stepping stones?

You like, look ahead to the future a little bit, and you start thinking: Okay, well, if that is where I want to be, I’ve gotta focus my energies on making sure that I achieve that goal.

Did you have that mentality? Did you think about that in that way? And did you start setting those kinds of goals.

Sam Smith 03:01

I never set goals around job titles. I set ambitions around applying for certain funding streams. I had an ambition that I wanted to go and work in the States.

I’d always wanted to study in in the States as a child. ANd it didn’t kind of work out at a university level, but I was able to secure a postdoc position in the States.

So that was always a long-held ambition.

I wanted to apply to do my own research. I had my own ideas about things that I wanted to investigate, so I set ambitions to apply for an early postdoc award, at which point I knew that there were opportunities to apply for mid-career awards and more senior awards as well.

So it wasn’t with a view to: If I do this, I would become a professor.

It was about: If I do this, then I would win funding that would enable me to do some cool science, do something interesting, solve a problem with regard to cancer prevention or cancer control.

So although I said: I want to become a professor. I want it, I didn’t really take actions to try and achieve that. It was more about those, the actions that I did take led me down the pathway of becoming a professor.

Julie Gould 04:24

As Sam got older, however, his ambitions changed slightly. He started a family, which meant…

Sam Smith 04:30

…things around career ambition became more important.

So titles equal a higher salary, obviously.

And I I did apply for promotions around when my first child was born, and when my second child was born.

But there was an ambition there to, at some point, achieve those, those goals of job titles, of associate professor and professor.

Julie Gould 04:55

How important is it to have a strategy in place in order to achieve and plan for that goal?

Sam Smith 05:03

I appear from the outside, and I probably am, very strategically focused. I have a plan for lots of things around my career and around my research group. That doesn’t mean to say that there isn’t flexibility there.

And I think that’s the the kind of nuance that I kind of, I sometimes struggle to convey.

So I have ambitions to apply for a particular funding stream.

But if that doesn’t come off, then that’s fine. You know, like, there are alternative routes around that. But it’s, it’s the work that I’d like to get done, the research and the topic that I’m kind of interested in, rather than the award of that particular fellowship, or whatever.

So yes, I do have a strategy, and I think things through logically. I think as I’ve got more senior in my role, it becomes cliche, but the ability to say no to stuff is becomes much more important, and sometimes it’s saying no to yourself as well.

So if an opportunity arises, I have to kind of have some sort of structure around my career to say: Is this something that I really want to take on? Just because it seems interesting for the next half an hour? That’s going to derail everything that I’m interested in for the next five years?

Julie Gould 06:36

But for Sam, a plan is not going to work unless you’ve got a support system in place to help you navigate it.

Sam Smith 06:42

You can plan as much as you want, unless you’re giving that support, that kind of sponsorship along the way, it’s really difficult to get through an academic part.

It’s so challenging, and it’s even more challenging now than it has been before.

Julie Gould 06:59

And do you think that because it’s now so much harder and than it ever has been for as you just said.

Do you think that having some kind of strategy and some kind of focus career plan in place is more important than ever?

Sam Smith 07:15

I think if you know that you want to go down the academic path or at least give yourself a fighting chance of getting an academic position, whether that be post-PhD or mid-career, etc, a strategy is always going to help. I guess.

What I’m trying to convey is that even with the best strategy, the environment is such at the moment that it can be difficult for even the strongest people to succeed in academia.

And what I don’t want to convey is that that there shouldn’t be this binary around, Oh, you succeeded in the academic path. Oh, you succeeded in an industry path, or whatever it is that you pursued.

All of those should be counted equally as successes. And so again, it comes back to this, I guess, flexibility discussion around if, if you want to go down an academic path, then, of course, yeah, great, and go for it.

But I would advise, even within that, a kind of career strategy around that, that you do things that will strengthen your case for moving into a different role that was perhaps outside of academia in the future.

And that’s something that I don’t think that I did particularly well as an early career researcher. But certainly something that I’m trying to instill within my colleagues here in Leeds.

Julie Gould 08:38

Sometimes people know exactly what career they want from an early age, and they can put goals and steps in place to make sure they get there.

This is how our second career story starts. Milicia Radisic is now a professor of applied science and engineering at the University of Toronto in Canada. But she started in Serbia, where access to resources was challenging.

Milicia Radisic 08:58

I knew pretty early, I would say very young, you know, maybe even in high school, that I wanted to have a scientific career, right?

I was really excited about mysteries and wonders of the natural world. I just didn’t know exactly what that meant or how I could do it, right? So, I had to leave my country.

Julie Gould 09:16

You left your home country. Was that out of choice, or was that out of necessity? Can you tell me a little bit where you came from and why you moved?

Milicia Radisic 09:26

Yeah, so it was definitely out of choice. I had to leave Serbia in the 90s, because back then, it was a very difficult time.

In Serbia it was really difficult not just to get journals, like journal papers, even to get books. You know, people who don’t remember the pre-internet time, we had to read papers in hard copy journals.

So you have got to have a subscription.

And if your country is under an embargo, not only is it not getting journals, but not getting food, you know.

So that was, that’s something that we have to think about also how, you know, in everything that’s happening in the world, how a regular person gets affected by, you know, politics, that’s about them.

And and so, yeah, I had to leave even if I wanted that knowledge, the current knowledge, and to read papers and to get the best textbooks and to have access to the latest equipment I had to leave Serbia because we were not able to get that in the 90s.

And then I came to Canada, to McMaster, to do my undergrad, and it was really wonderful.

And Canada is a really wonderful, welcoming country, and McMaster really enabled me to grow as as a student,

Julie Gould 10:58

Because Milicia knew and understood how scarcity could impact her life, she was determined to work in places and with people that would help her succeed in her dream to becoming a successful scientist.

And after spending time at McMaster in Canada, Milicia took on a position in Robert Langer’s laboratory at MIT in the USA.

And both these decisions were incredibly strategic for her. Milicia likened her, or any career path, to a large cargo ship sailing across an ocean.

Milicia Radisic 11:25

If you just let the ship sail, it’s gonna end up somewhere else.

You gotta make sure you navigate properly, but keep in mind that it’s really hard to make that turn.

So it’s very, turning in a huge ship it’s really slow. So you gotta like, always keep that eye on the target.

Julie Gould 11:43

One way to keep your eye on the target and to help you get there is to make sure you have a vision, a big picture view of your research and how it fits in with the wider research environment.

And this is something Milicia has spent a lot of time thinking about.

Milicia Radisic 11:56

How am I going to develop my vision as a scientist? This is something that you cannot cram.

You cannot be like, Oh, I’m going to work on it for two weeks in January, and then it’s going to be done.

This is something that really requires a lot of thinking, a lot of reading, going to conferences, talking to other scientists. Really, seeing where is that gap of knowledge, and how can my skill set push that boundary further?

And that’s what we do all the time in writing proposals, right? And winning the money. You’re always thinking about kind of, what’s that next gap?

And that is really deep thinking that requires a lot of time. And as I said previously, you won’t be able to do that 9-5 at your desk. It’s not that kind of job. This is something that, and you know, thinking about that kind of percolating those ideas, that strategy is always, always in your head.

Julie Gould 12:48

Today, as a supervisor, Milicia tries to encourage her team to make sure their ships are sailing in the right direction and are easy to manoeuvre.

And one of the ways she helps her researchers is by making sure she works on a publication strategy with them.

Milicia Radisic 13:01

How am I going to go about what makes sense to publish, when and how, right?

So that you can have on your CV that track record of, both, you know, you really have some high impact, transformative papers, and you also have papers that are worth publishing, but they may not be in Nature, right? So that way you will show your productivity.

Whoever is your PhD or postdoc supervisor, they can strategize with you about the publication plan, right?

I usually try to put my people on both kind of lower risk and higher risk projects, because you don’t know if the higher risk project will work out.

So I want to make sure they have something. And that’s where you’re really your supervisor can help. And working in teams, so you also can contribute to many different publications is very important.

And when we were assistant professors at the University of Toronto, we would start, we will have these like workshops, just like, How do you succeed as an assistant professor?. There was this one point that was made by Scott neighbor, and he said, quality is important, but never underestimate the importance of quantity, right?

So you don’t want to put all of your eggs into one basket, and if that Nature paper doesn’t work out, you have nothing, right?

So you gotta, like, have your project, your question that you’re working on, and then think about what makes sense to publish, when and how, right?

So that you can have on your CV that track record of both you know, you really have some high impact, transformative papers. And you also have papers that a worthy publishing but they may not be in nature, right?

Julie Gould 14:44

But how do you make sure you’ve got all the papers ready for when it comes to applying for tenure, for example?

Like Milica said before, don’t put all your eggs in one basket

Milicia Radisic 14:55

When you’re an assistant professor. If you do that and you take your three students that you have and you put them all on working on he Science paper or Nature paper, you may end up with literally nothing because you don’t have that big infrastructure that your professor had was established.

And just being able to put all of those resources and people in, and the time it takes to do that, you literally may have nothing.

And to get tenure, you need to attach five papers to dossier, usually five papers. So you really have to be strategic, like I have these students. These are my projects. These are my questions. I’m going to parse it out, and I’m going to this smaller question answer in this paper. I’m going to submit it to this journal that I know is fast in reviewing papers.

So you really have to think about your big vision, your big project, that you only have a few trainees, and how you’re going to parse that out so that you get publications that you can attach to your tenure package.

And these publications shouldn’t Look like random collection of papers. They should all present a coherent vision. So you really have to think deeply about how to break that out,

Julie Gould 16:06

As we heard from Sam Smith earlier in this episode, knowing when to say no is important when it comes to thinking about what you can spend your time on.

Milicia Radisic 16:14

So then you’re keeping your eye on that prize, like: How do I get tenure, right? And the best thing you got to ask people, this is all prescribed, right?

The beauty of getting tenure is that you should know exactly what you need to do to get tenure. If you don’t know, then your department is doing something wrong.

It’s not you, it’s your department is literally not doing their job.

So because this is all prescribed in guidelines, it’s all written, and your department chair is supposed to be able to communicate to you how, what do you need to achieve to get that.

And so it’s very clear, right?

You need some papers. You need to have your own grants. So you’re just focusing on that exclusively. In my mind, that’s like, the best strategy. Don’t get diluted, and don’t be like, Oh, I’m going to start a company.

Don’t start a company then You can start a company after you get tenure, and so, or if you’re just thinking, oh, There is time I’m going to do that next year, and then you may not get tenure. You gotta do it now.

Julie Gould 17:25

Sam and Milicia are both very focused academics with clear paths, plans and strategies. But this kind of strategizing doesn’t suit everyone.

Some people prefer to roll with the punches, go with the flow, see what life throws at them, and take opportunities as they come.

And this is what we’ll hear about in the fourth episode of this series on career planning.

But before you go, here’s our sponsored slot from the International Science Council on career development for early and mid career researchers in an ever-evolving scientific landscape.

Thanks for listening. I’m Julie Gould.

Izzie Clarke 18:12

Hello and welcome. I’m science journalist Izzie Clarke and in this podcast presented in partnership with the International Science Council, with the support of the China Association for Science and Technology, we’ll be discussing the power of the digital aid and artificial intelligence known as AI, its importance to careers in science, as well as its potential threat to the scientific enterprise.

Today, I’m joined by Mercè Crosas, Director of Computational Social Science and Humanities at the Barcelona Supercomputing Center and President of the Committee on Data of the International Science Council, known as CODATA.

Mercè Crosas 18:52

Hello.

Izzie Clarke 18:53

And Mohammad Hosseini, Assistant Professor of Ethics at Northwestern University in Chicago, and member of the Global Young Academy.

Mohammad Hosseini 19:01

Hi, how are you?

Izzie Clarke 19:03

Very well, thank you. I think a question to both of you, to start things off, is why is now a critical moment to reflect on how digitalization and AI are shaping scientific careers?

Mohammad Hosseini 19:16

I think we are seeing more and more data-driven decision-making by researchers, which sometimes also trickles down to national or local decision-making, which is good, but in terms of scientific careers, this means that we need to train researchers in new skills.

And this has always been the case. But because of the tipping point, things are moving so fast that we can hardly catch up. Machines are becoming so capable that they can displace or replace human workforce in science. We are now in a sort of critical moment to discuss digitalization and explore who benefits from these technologies, who may be left behind and how we can ensure transparency and equity in their use.

Izzie Clarke 20:03

Mercè, what are your thoughts?

Mercè Crosas 20:06

One of the things first is that AI also has been used in science already for quite a long time, and the change has been happening progressively. It is true that now there is an exponential use of AI used as for methods in a lot of the scientific production.

So, from the exploration of the literature review to trying to figure out the research question, to data processing and data collection, and then the analysis itself, but also then the publication of the scientific results. I mean, I guess that tipping point that Mohammad was talking about, it has a much broader impact than ever before.

Izzie Clarke 20:43

There’s a lot of things to consider here. You mentioned publication there and we will get onto that in a moment. But in terms of opportunities, what are the opportunities that you see emerging from this for early- and mid-career researchers and how that is changing that AI-driven scientific landscape?

Mohammad Hosseini 21:07

I guess opportunities are mostly around making new discoveries and doing things that would be even a dream five years ago. Any area that could benefit from modelling, we are moving much faster now. This is an opportunity, especially for early- and mid-career researchers who may be more adept in using AI, but it comes with certain trade-offs. Finding opportunity in this new dynamic requires a new kind of curiosity that we are not trained in. But I think we should try to find tasks in research contexts that cannot be automated and try to excel in such tasks.

For example, my area of research, I’m an ethics researcher. Writing a well-argued paper is already automated. But mentoring, teaching an in-person class, which is also interactive and engaging, or conducting interviews to collect data and get new insights from people’s lived experiences — these are tasks that cannot be easily automated. And I think we need to find these group of tasks in our own research context and try to excel in that.

Izzie Clarke 22:15

And Mercè?

Mercè Crosas 22:49

I don’t see much the risk of scientists or early-career scientists, mid-career scientists, to be substituted. What I see is opportunities to new research questions that a lot of scientists from previous generations couldn’t even think of asking, right? So, no, it’s not so much just that, well, now we can apply these tools, but that we can think about some fields in a whole different way. In biomedicine, in climate change, in physics and biology for genetics, that can change with the use of AI and new types of data.

Izzie Clarke 23:08

I think we are seeing that there are a lot of different ways that we can turn to AI and tackle different tasks, and we’ve talked about re-skilling. So, what do you think early- and mid-career researchers in the scientific fields need to be mindful of, and where can they get support?

Mercè Crosas 23:43

It’s more important than ever to be very rigorous in science and to understand that, at the end, whether we use AI or we use other tools, science is what we do, and science is inference and science has to be public. The methods, the data and the way we do it has to be verified by others.

It means that, again, we don’t just use the AI tools to give us answers, but we need to become more specialists in how we validate those answers. And for that, we need to still be more prepared about the theory of the fields where that we do research and the rigorosity of the outputs.

Izzie Clarke: 23:49

Yeah, I mean, Mohammad, I’d love your thoughts on this as well because I know that this is something that you pay a lot of attention to.

Mohammad Hosseini: 25:05

Yeah, absolutely. And I also want to go back to what Mercè said here. Yes, it is important to think about theory, and at the same time, there’s a lot of people who now argue that because of this rise of data-driven science, we are seeing the end of social theory. Theory is not really as important because people can just collect data and do data mining to see what is relevant without even having had a hypothesis prior to their data collection.

And I think that’s a remarkable development that requires a lot of careful consideration and attention. I think one of the challenges I also want to highlight is the fact that we have access to different resources, depending on location. We also have disparities in terms of what institutions provide. I have the privilege to be based in an affluent private university in the US that offers free access to various AI models, but this is not the case for millions of other researchers.

And this disparity puts many other people in a disadvantaged position. Many universities don’t even have a general policy for the use of AI models. If I was in such university, I would really try to speak with the university administration or library to ask them to provide guidance and training.

Mercè Crosas: 25:46

To follow up on the danger of becoming too data-driven. I don’t accept that that’s the way that we need to go, right? The results is the intersection between the theoretical model and this data-driven approach. But in terms of using generative AI or new types of AI tools, I think that Europe has pretty different approach than other places.

And there is now undergoing the development of a new strategy of AI in science and science for AI. We need to be careful about what kind of AI tools we use, whether they have clear definition of what data has been used, whether they are open source, whether they focus on trustworthy AI, and I think that’s very important.

Izzie Clarke: 26:04

I wanted to pick on something there as well. We talk about how we are using AI in work and publishing, as well. So Mohammad, what are the things that you think early- and mid-career researchers should be mindful of when it comes to publishing and the use of AI?

Mohammad Hosseini: 27:14

Yeah, I think one of the things that we should be really mindful of is what is the task that we are offloading to AI? What is the task we are asking AI to do? When this AI boom began, AI was mostly being used at the end of your research process, like at the point of copy-editing and improving readability and so on.

But now we are offloading these important tasks to AI, and next time when you want to think about your next research question, instead of thinking deeper about the textbooks you read or the new articles you read, you’re like, ah, let me ask what AI has to say about it. It becomes very addictive, and I would encourage researchers to be aware of the tasks that they are delegating and ask themselves, is it worth it?

My suggestion is don’t just publish something for the sake of publishing something unless you have something really important to say. Think about who are you citing. If you’re using AI to find literature, make sure that you read the content that you are citing, because many times these citations are irrelevant.

Izzie Clarke: 27:39

And I think that’s a good point. Yes, there are ways that we can use AI that might be helpful in some points, but keep some of those skills active and to make sure that you are doing due diligence in other ways, as well.

And I think that probably brings us onto a discussion on credibility. So, within your field and to the wider public, what does it take to maintain credibility in this digital age? Mercè?

Mercè Crosas 28:16

Well, I think it’s very easy. I mean, you had credibility when you can communicate it, when you fully understand it and what you’re working on and it’s not been generated by something else that you don’t understand. Going back to the values of science and open science, that it is as transparent as possible, that anybody else can verify what you’ve done from how you have applied the AI model, the method, the data that you’ve used, the workflows, fair principles for findable, accessible, inter(operable), reusable data. But also software so that what you’re using is shareable, is findable by others and can be verified.

Izzie Clarke 28:30

But there are lots of exciting ways that this can be a tool for transforming science and digitalization, as well. So, Mercè, how do you see the role of science communication growing as technology grows, as well?

Mercè Crosas 28:52

Well, so, science communication, we still need to do a lot of work on that for society. And there are already expectations that are possibilities or opportunities for AI to play a role also in helping summarizing a lot of the science output and make it more accessible to broader audience. So, I think that can be interesting.

Izzie Clarke 28:57

And finally, what gives you both hope for the future of science in this digital world? Mohammad?

Mohammad Hosseini 29:23

I think what gives me hope is a new generation of researchers who speak up. We are observing a new generation who dares to say what it thinks and is willing to pay a price for it. I’m in the US and I see all kinds of big companies and how they can influence the research landscape and universities and all of that. So, it’s very important for me to see that.

Izzie Clarke 29: 35

And Mercè?

Mercè Crosas 29:36

So, I think that we have more tools to understand how we work, how we collaborate, what new questions we can ask in science. And I think that gives hope for better science if we don’t lose what science is and we don’t lose these values of open science, but also taking advantage of this new type of AI methods.

Izzie Clarke 29:44

Thank you both so much for joining me.

If you’re an early- or mid-career researcher and you want to be part of the conversation on the future of AI, join the International Science Council Forum for emerging scientists.

Visit the website council.science/forum to find out more. I’m Izzie Clarke, and next time we’ll be discussing how early- and mid-career researchers can help protect our ocean and the power of a transdisciplinary approach to do so. Until then.

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