kla-s
Joined 4 years ago
Joined 4 years ago
Always thought it interesting that for certain diagnosis wearing them until end of life is indicated according to current guidelines and we don’t expect maladoption (weakening of the connective tissue). Id kind of prefer/would be interested in a workout based approach, also interesting what knock on effects such workout would have on the larger system
Im not quite sure if the current paradigm of LLMs are robust enough given the recent Anthropic Paper about the effect of data quality or rather the lack thereof, that a small bad sample can poison the well and that this doesn’t get better with more data. Especially in conjunction with 4) some sense of truth becomes crucial in my eyes (Question in my eyes is how does this work? Something verifiable and understandable like lean would be great but how does this work with more fuzzy topics…).
Thanks @missedthecue, i wasn't aware this was productized and tested (though hoped it was)
And maybe as a Bonus what do you make of the smaller (relative) taxrate the bigger fish (companies/wealthier individuals) pay?
Remote: Yes
Willing to relocate: Yes
Technologies: Python (PyTorch, TensorFlow), Reinforcement Learning, Medical Imaging, Machine Unlearning, Physics-informed ML, Quantum Computing, Protein Structure Prediction, NLP, Java, SQL
Résumé/CV: https://drive.google.com/file/d/1b9UfeqFvEu0t4sTGNd8yiJ1m60x...
Email: [email protected]
Hi, Kilian here :) ML engineer / research-minded generalist with a recent M.Sc. in Informatics from TUM, including a thesis on machine unlearning in medical imaging (@Harvard Medical School). I’ve worked on problems like reinforcement learning for tumor landmark detection in MRI, and deep learning for physical systems and protein structure prediction.
I like forming own ideas and following them through — from literature review to implementation, evaluation, and iteration. I can reproduce papers, fine-tune models, explore new methods, and design experiments that actually test hypotheses. I’m especially motivated by early-stage work that blends research thinking with practical engineering.
In team settings, I tend to gravitate toward coordination and planning roles, and outside of work, I’ve led a 300+ member volleyball department for several years — which taught me how to manage people, not just models.
I'm looking for roles at the intersection of research and engineering, eg early-stage AI projects where curiosity, ownership, technical depth matter and impact-driven development are valued.
Isn't the same meta process at play when thinking about more fuzzy topics?
Want to talk about anything? -> [email protected]