KEYWORD |
Area Ingegneria
AI Agents in a Biological Lab: Helping Scientists Perform Complex Tasks
Tesi esterna in azienda
Parole chiave AI, LARGE LANGUAGE MODELS, MACHINE LEARNING
Riferimenti RAFFAELLO CAMORIANO
Riferimenti esterni Riccardo Volpi, Ph. D., Matteo Zanotto, Ph. D.
Gruppi di ricerca Arsenale Bioyards, DAUIN - GR-23 - VANDAL - Visual and Multimodal Applied Learning Lab
Tipo tesi EXPERIMENTAL, RESEARCH AND DEVELOPMENT, START UP
Descrizione Arsenale Bioyards is aiming to make biomanufacturing via precision fermentation economically viable at industrial scale – for the very first time. Artificial intelligence plays a key role in our company: our goal is to automate every possible task, to let humans focus on the hardest challenges. We are looking for a motivated student to carry out a six month project with us, in which s/he will develop novel solutions to help us build the AI operating system behind our Lab. S/He will be using LLMs (Large Language Models) to this end, learning and building on techniques such as RAG (Retrieval Augmented Generation). The methods will be developed in Python and the student will have access to high-performance GPUs. The student will have the chance to work in a vibrant startup environment, and to develop impactful solutions to important problems.
Location: Pordenone / Work From Home friendly.
Duration: Six months
Note: paid thesis internship
Contact: raffaello.camoriano@polito.it
Goals
● Literature review
● Formalizing our constrained optimization problem
● Implementing a workable solution to our problem, drawing from the literature
● Testing on both simulated and real data, focusing on public benchmarks and, whenever possible, experiments carried out in our Lab
● (Optional) Designing and implementing solutions that address limitations found in existing methods from the literature
● (Optional) Depending on the outcome of the project, we will write a research paper about it
References
● Clemens Kreutz, Jens Timmer, Systems Biology: Experimental Design, The Febs Journal, 2009
● Peter I. Frezier, A tutorial on Bayesian Optimization, arXiv:1807.02811 [stat.ML] 2018
Conoscenze richieste ● Motivated to work in a dynamic, fast-pace environment
● Strong Python skills
● Has worked on machine learning projects
Scadenza validita proposta 29/01/2026
PROPONI LA TUA CANDIDATURA