Sebastian Sydlik Birkenholz
Senior Digitalization Professional Data Scientist & Ai Specialist @August-Wilhelm Scheer Institut
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WORK HISTORY
Senior Digitalization Professional Data Scientist & Ai Specialist @August-Wilhelm Scheer Institut
Following years of building analytical methods in research contexts, this role represents a deliberate expansion into modern data science and AI infrastructure — skills increasingly relevant across all quantitative scientific domains.• Production-grade analytical pipelines: Designed and implemented robust, reproducible ML pipelines for real-world biological sensor data — emphasizing clean code standards, scalability, and maintainability. The ability to build analytical infrastructure that others can use, audit, and extend is as relevant in drug development as in any other data-intensive field.• Applied ML and AI: Hands-on experience with the full supervised learning workflow — feature extraction, model selection, validation, and deployment — applied to noisy, real-world acoustic time series data. Developing practical fluency with modern AI tools and understanding their underlying principles is increasingly a core competency for quantitative scientists in any domain.• Science-industry translation: As Head Startup Coach in the Future Greentech Incubator, continued applying the strategic and communication skills developed in previous coaching roles — supporting deep-tech founders in translating scientific work into viable products.
EDUCATION
University of Zurich
Doctor sc. nat. - PhD, Neurowissenschaft
Maastricht University
Bachelor of Science - BS, Biomedical Sciences
The University of Göttingen
Master of Science - MS, Neuroscience
ABOUT SEBASTIAN SYDLIK BIRKENHOLZ
My path into quantitative science began in a Cambridge neurophysiology lab, where as a competitively selected Amgen Scholar I recorded miniature synaptic events for the first time. The nature and regulation of these events was so fascinating to me that it set the direction for everything that followed — a Master\'s at the Max Planck research school in Göttingen, a PhD at the University of Zurich, and eventually a transition from wet-lab experimentalist to computational scientist, driven by a simple realization: answering the questions I cared about required methods that didn\'t yet exist.That principle still guides my work. I identify the most important unsolved problem in the room and build whatever is needed to address it.During my PhD and postdoc, that meant developing novel analytical methods from scratch — custom signal processing pipelines, simulation-based benchmarking frameworks, and biophysical models of receptor kinetics — because existing tools were insufficient to extract reliable quantitative signal from noisy electrophysiological data. This produced a novel event detection algorithm that outperformed standard methods at low signal-to-noise ratios, and a statistical framework for identifying discrete quantal steps in postsynaptic responses.At a certain point the biggest leverage shifted — not to the next pipeline, but to understanding how scientific work creates real-world impact. Several years coaching deep-tech and life science startups gave me a clear view of how scientific innovation translates into products, and what separates research that matters from research that doesn\'t. That perspective now shapes how I approach every technical problem.I am now returning to what I do best: building quantitative models of complex biological systems. My expertise sits at the intersection of mechanistic modeling, statistical inference, and signal extraction from noisy biological data — grounded in neuroscience and biophysics, and actively directed toward drug development and quantitative pharmacology.What I bring that is relatively rare: hands-on method development at the algorithmic level, deep biological intuition from years of experimental work, and the strategic perspective to know which problems are worth solving.
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