Academic Careers and Real Life: When Full Availability Isn't an Option
Working with a chronic illness in academia often means navigating expectations that were designed around full availability. In the article KT Boost Fellow Jan Pennekamp shows how communication, flexibility, and the right support can make an academic career more sustainable. His interest in leadership development adds another perspective: researchers do not only have to find ways of working that suit their own circumstances; as leaders, they also help create environments in which real people with different needs and responsibilities can do their best work.
Jan is a postdoctoral researcher (equiv. staff scientist) at RWTH Aachen University, where he works at the intersection of computer science and healthcare-related research, with a focus on information security and privacy. In his Boost-funded project, he investigates whether synthetic healthcare data can reliably reproduce findings from real patient data. One year after the decision to award him a KT Boost Fellowship, Jan got a permanent position at RWTH Aachen.
In 2026, our colleague Dr. Birte Seffert is back on the train, travelling across Germany to visit the 15 KT Boost Fellows who started their fellowship in 2025. Season 2 of “Keeping up with the Boost Fellows” continues to shine a spotlight on the journeys, challenges, and achievements of our Fellows. It also shares practical lessons for PhD students and postdocs who are building their own path toward independence.
Jan Pennekamp
What are synthetic data, and why should we care?
Healthcare data can help researchers identify patterns, understand diseases, and develop new approaches to diagnosis and treatment. At the same time, they are highly sensitive, as they may reveal genetic information, medical histories, or stigmatized conditions.
Synthetic data are artificially created representations of original data. In healthcare research, they may help reduce privacy risks while still allowing meaningful analysis.
Jan’s Boost-funded project asks whether synthetic data only look similar to real data, or whether they lead to comparable research findings. Together with his colleagues, he compares real-world datasets with synthetic data and tests whether established clinical findings can be reproduced.
As Jan puts it, “there is currently no golden path that one can simply trust blindly”. Complex relationships, such as connections between different blood values, may be lost or distorted. His project therefore asks what researchers need to know before synthetic data are used more widely in healthcare research.
Working across disciplines means staying in the conversation
Jan’s project brings computer science into contact with healthcare research and clinical expertise. For him, that is one of the most interesting parts of interdisciplinary work. A real-world use case makes abstract questions tangible. Even if he is not an expert on liver diseases, for example, clinical collaborators can explain what certain data patterns may mean and why they matter.
At the same time, interdisciplinary collaboration does not work simply because several disciplines are involved. It requires active translation, regular exchange, and mutual interest.
Jan’s TOP 3 advice for early-career researchers working across disciplines
Check whether you are really talking about the same thing.
Different fields may use the same words differently or assume different things without saying so. Jan remembers a collaboration from his PhD phase where both sides thought they were moving in the same direction, only to realize much later that they had understood a core point differently. The lesson: do not wait a year to compare results. Stay close enough to notice misunderstandings early.
Build collaborations where both sides have a real research interest.
Computer scientists are often asked to “support” other fields simply because they can work with data. But support alone does not automatically establish a strong research collaboration. For Jan, the best interdisciplinary projects are those where both sides gain something scientifically and where the project opens questions that are interesting for everyone involved.
Look beyond the next obvious step.
Especially in established fields, collaborations can become very incremental: what is the next logical step, what helps the current project, what fits the next qualification goal? Jan’s experience is that interdisciplinary work becomes most productive when partners also ask what the second or third step could be. What becomes possible if both fields take the collaboration seriously?
Working with a chronic illness in Academia: make the invisible negotiable
Jan completed his PhD part-time due to chronic illness. In his field, where full-time contracts are common, this was unusual. He says it helped him to communicate his situation from the beginning, and he was fortunate to have a supervisor who was open to flexible solutions.
His reflections are practical, and they matter beyond one individual story. Many researchers work with health conditions, care responsibilities, disabilities, or other circumstances that affect how they can work. Academic systems often assume full availability, but real careers are built by real people.
Four reminders from Jan’s experience
Communicate what others can’t see.
Jan says one of the most important lessons was that others cannot look inside you. If workload, meetings, or expectations become too much, it often has to be said explicitly. This can feel uncomfortable, but it is the basis for finding solutions.
Know your working time and protect it.
Jan kept track of when and how long he worked. This helped him see patterns and avoid unintentionally doing too much. When colleagues are still in the office, leaving on time can be hard, especially if the work is enjoyable. External anchors, such as physiotherapy appointments or fixed timeslots for leisure activities, helped him make the end of the working day real.
Use available support without apology.
Jan points out that Germany does offer support options, for example, through integration offices or workplace adaptations. In his case, this included equipment suited to his needs. Depending on the situation, support may look very different, from technical equipment to adapted workplaces or flexible schedules.
Decide for yourself what to disclose.
Jan has sometimes chosen to mention part-time work or disability-related circumstances because otherwise his CV might be compared to full-time trajectories without context. But he is clear that this is a personal decision. There is no single right way to handle disclosure.
His overall message is not that everything becomes easy if one communicates well. Rather, it is that good working conditions, flexibility, and an environment where people can speak openly make a difference.
Why leadership training helps, even before you lead a group
Since moving into a permanent position at RWTH Aachen, Jan is also taking part in further training focused on leadership skills.
Leadership in academia often starts before someone has a formal leadership title. Postdocs and, depending on the work environment and discipline, PhD students supervise students, coordinate projects, support colleagues, handle conflicts, and become informal points of contact. Yet many of these tasks are learned by doing.
For Jan, training helps because it gives names, concepts, and techniques to situations researchers already encounter. How do you deal with conflict? How do you manage stress? How do you understand the leadership relationship with your own supervisor? How do you recognize problems before they escalate?
The exchange with others is just as important. In training settings, researchers hear what has worked elsewhere, what has gone wrong, and which problems are not unique to their own group. Sometimes this is reassuring. Sometimes it is a useful reality check.
Jan also values courses on topics such as project management, organizational development, or mental health and wellbeing. Researchers often have to write proposals, build budgets, prepare timelines, or coordinate work packages long before anyone has formally taught them how. Even small insights can save time later, improve collaboration, or help prevent misunderstandings.
That is why he recommends using professional development offers when available. Not because every course changes a career, but because even one useful concept can make the next project, conversation, or conflict a little easier to handle.
What the Boost Fund makes possible
For early-career researchers, Jan’s story also shows that independence is not only about funding a project. It is about learning to collaborate across boundaries, making working conditions explicit, using support structures, and developing leadership skills before everything depends on them.
The KT Boost Fund is a joint program of GSO and the Klaus Tschira Stiftung for postdoctoral researchers in the Natural Sciences, Mathematics, and Computer Science. It offers flexible funding for risky and interdisciplinary research on the way to academic independence. Funding can be used to hire staff, buy equipment, or build collaborations, tailored to the research project. Fellows also benefit from career development, coaching, and a growing peer and alumni network.
About the KT Boost Fund
The Boost Fund supports postdoctoral researchers and early group leaders in Germany with flexible funding for independent, often higher-risk and interdisciplinary projects, combined with career development opportunities and access to a strong peer network.
The program addresses a critical phase after the PhD, where researchers are expected to develop independence, but often lack the resources and flexibility to do so. It creates space to explore new directions, build collaborations, and take responsibility early on.
