Testimonials
Ultimately, I’m driven to uncover the mathematical rules that control neural communication.
- Programme
- Machine Learning and Neural Computing
Sander Keemink is an assistant professor at Radboud University.
Can you introduce yourself?
My academic journey began at the University of Twente, with the 'Advanced Technology' Bachelor's. After a while, neuroscience sparked my interest. Building models was an essential part of my education and this skill was especially relevant in in understanding how neurons function. The combination between physics and computational science fascinated me, for example when creating models to explain brain function. Alongside my technical studies, I followed some business-related courses, initially considering a corporate career. However, my Bachelor’s thesis work at an international research facility in Japan at the National Institute for Materials Science (NIMS), where I researched nanoscience, shifted my focus and interest to research.
I moved to Utrecht to complete a Research Master's in Neuroscience and Cognition, but I also spent time in the US, conducting research at several universities. Following that, I obtained my PhD in Scotland and Germany, followed by two postdoctoral positions, including one at the Champalimaud Institute in Lisbon. My research focuses largely on understanding the brain’s complex workings and applying this knowledge to artificial intelligence. Currently, I coordinate the Machine Learning and Neural Computing Master’s specialisation and I teach two courses on neuroscience: one in the Bachelor's programme and one in the Master’s programme. Additionally, I supervise students working on their theses.
Why did you choose to study/work in this field? What makes this field so interesting?
Most people are curious about how humans work. During my studies in physics, I learned about quantum mechanics, which introduced concepts like particles being uncertain in their location and trajectory. This idea sparked a philosophical question for me: what determines how this uncertainty is resolved? This, in turn, led me to think about what it means for a human to observe something. From there, my fascination with understanding the brain grew, as it became clear how much remains to be discovered about how it functions. Neuroscience offers a unique combination, as understanding the human brain requires knowledge from a lot of different disciplines - in which the modelling techniques I had learned so far are very useful.
What are you currently doing your own research on?
My role is split between teaching and research. In my research, I try to understand how the brain works and how we can apply that knowledge to artificial intelligence. In AI research, large networks are often built, but there’s still a lot of uncertainty about how these networks make decisions. I am particularly interested in working with smaller, more manageable networks of a few hundred neurons, where we can more easily see what is happening. I apply mathematical methods to these networks and, over time, aim to scale this understanding to larger systems. The focus of my research is on how information is given, processed, and used by these networks. Ultimately, I’m driven to uncover the mathematical rules that control neural communication.
What advice do you have for students making their study choice?
It's important to consciously give yourself the space to discover where your passion lies. My advice is to take time to relax and allow your mind to process everything you have learned, especially during weekends or holidays. In the hustle and bustle of academic life, finding time for yourself to unwind and think can help provide clarity on your direction. My best tip is to be patient with yourself and try not to rush.
What is the best part of being a lecturer?
One of the most fulfilling aspects of teaching, especially when supervising theses, is watching students deepen their understanding. There is a moment when things ‘click’ for them—they are no longer just learning because they have to, but because they are developing new questions, methods, and ideas. It is incredibly rewarding to see students transition from absorbing knowledge to contributing new perspectives. This is particularly exciting in research, where I guide students over a span of multiple months, helping them to go beyond simply mastering the material and begin creating new knowledge.
As a tech-savvy individual, it was really important for me to work hands-on with the new technologies and tools that are being developed.
- Previous education
- BSc Artificial Intelligence (University of Groningen)
- Programme
- Machine Learning and Neural Computing
- Study end date
Thalia Najjar was a Master's student in AI at Radboud University.
Where do you work now and what does your job entail?
I currently work at Itility in Eindhoven, an IT consultancy firm. As a Data Engineer, I get to work on different projects for clients in the agrifood, energy and manufacturing industries. What my job exactly entails highly depends on the project, which allows me to keep challenging myself by learning about new fields of work while unlocking new technical and functional skills.
Why did you choose to work in this field?
Although I find research very interesting, I always knew that on the long run I would prefer to work in the industry. As a tech-savvy individual, it was really important for me to work hands-on with the new technologies and tools that are being developed. Consultancy was not a path that I specifically wanted to pursue, but Itility sparked my interest with a "consigneer" role, which stands for consultant engineer. Every employee has two hats: the engineer hat, working on solving a given technology problem and the consultant hat, working with and for a customer to bring value to them. Moreover, consultancy was appealing to me for a first job as I get to work on very different project, thereby discovering diverse industries and technical tools and broadening my horizons.
What did you learn during your studies that you now use in your work?
On a technical aspect, I am currently working on a GenAI project, creating a company chatbot. Thus, all of the programming, AI foundations and NLP principles that I am using have all been covered in my educational journey. Ethics for AI is always omnipresent in every step of an AI project.
On a non-technical aspect, my studies taught me way more than I realised at the time. First and foremost, working in a team is an essential skill that we learned at University. Communication, critical thinking, time management, presenting and writing are other skills fostered at University and are essential in my work, or in any other work.
How did you experience this programme at Radboud University? In your opinion, what made this programme special?
What I appreciated the most in the Cognitive Computing Master's specialisation (the former Machine Learning and Neural Computing specialisation, ed.) is the flexibility that we, as students, have to shape our course programme as desired. As a curious person, I chose many courses from different Master's and genuinely enjoyed learning more about diverse topics.
Furthermore, every University employee that I have worked with was always very kind and helpful, as they all seemed to have the students' best interest at heart.
What advice do you have for students choosing a Master's programme?
Follow your instincts and go for what you believe to be the best path for you. Also, don't be afraid to change directions if you notice that after all this Master's was not made for you. No matter which programme you go for, use this time to experiment new things to figure out what you would like your future career to be, and also to discover yourself better. Most importantly, make sure to prioritise yourself and your mental health.
Study Association
CognAC is the study association for Artificial Intelligence students at Radboud University. They organise fun activities, as well as activities and services focused on study and career. Want to know more? Check out the website!