Enrico Trombetta is a PhD student at the University of Tokyo funded by the MEXT scholarship, designing neuromorphic hardware for spiking neural networks at the intersection of computational neuroscience and machine learning. He combines a fast-tracked MSc in Cognitive Science (110/110 completed in 13 months) with a First Class Computer Science bachelor's, and brings nine years of experience spanning NLP, reinforcement learning, knowledge graphs, and applied ML in industry and academia. Past roles include research on augmenting language models, modelling epidemics with deep RL at Glasgow, a software engineering internship at Morgan Stanley, and ML-driven behavioural neuroscience work presented at international conferences. A competitive programmer and CyberChallenge participant, he pairs rigorous theoretical grounding with practical engineering, mentorship experience through LeadTheFuture, and a knack for translating biological insight into hardware-aware algorithms.
9 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Cognitive Science - Fundamental Behaviour Neuroscience, 110/110, Master of Science - MS, Cognitive Science - Fundamental Behaviour Neuroscience, 110/110 at Università di Trento
Diploma Istituto Tecnico e Professionale, Informatica e Telecomunicazioni, 100/100, Diploma Istituto Tecnico e Professionale, Informatica e Telecomunicazioni, 100/100 at ITT Vittorio Emanuele III
Computer and Information Systems Security/Information Assurance, National Cyberchallenge contestant in Rome as a representer of Naples., Computer and Information Systems Security/Information Assurance, National Cyberchallenge contestant in Rome as a representer of Naples. at Università degli Studi di Napoli 'Parthenope'
University of Strathclyde
Bachelor's degree, Computer Science, First Class Honours, Bachelor's degree, Computer Science, First Class Honours at University of Glasgow
SLIM is the Sea Lice Model associated with a funded BBSRC project on the evolution to resistance to treatment in sea lice (BBR009309). We aim to integrate an epidemiological and genetic model of sea lice with a model of treatment decision-making by different salmon farms.
Contributions:8 releases, 37 reviews, 370 commits in 1 year 2 months
Contributions:277 commits, 6 pushes, 1 branch in 9 months
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