Descrizione:
Cambrex Profarmaco Milano is looking for a curious and dynamic intern interested in Artificial Intelligence.
For our Site in Paullo (Milan) we are looking for a trainee with expertise in ML methods and AI to join our Analytical Development team for an industrial secondment focused on building an AI-powered HPLC recommendation engine. This project leverages proprietary pharmaceutical data to develop machine learning models that predict optimal chromatographic conditions for new compounds.
Responsibilities:
Design and curate a structured experimental knowledge database linking molecular representations (SMILES, InChI, fingerprints), physicochemical descriptors, chromatographic method parameters, and experimental outcomes, with a focus on data quality, reproducibility, and design suitable for ML;
Develop a feature selection pipeline for classical descriptors (RDKit, Mordred) and learned representations (molecular fingerprints, graph-based embeddings);
Research, train, and benchmark predictive models for chromatographic outcomes (retention behaviour, mobile phase strength, column selectivity class), exploring both interpretable models and state‑of‑the‑art approaches;
Design a molecular similarity module grounded in chemical space geometry, evaluating distance metrics and embedding spaces for nearest‑neighbour method retrieval from historical data;
Build a recommendation engine that unifies predictive modelling and the similarity module, with uncertainty quantification, confidence scoring, and explainability to support trust and adoption by domain scientists;
Extend the engine into an agentic LLM interface that allows natural language interaction with the underlying models and database;
Validate the system on held‑out experimental data, document methodology to publication standard, and present research outputs to both technical and domain‑expert audiences.
Qualifications and Skills:
Degree in Computer Science/Engineering and currently enrolled in a PhD or Postdoc program in Machine Learning, Artificial Intelligence, Computer Science, or a closely related field;
Deep understanding of ML and AI.
Strong research instincts: ability to identify the right problem formulation, design‑controlled experiments and critically evaluate model behaviour rather than just benchmark metrics;
Proficiency in Python and relevant libraries (scikit‑learn, pandas, NumPy, PyTorch, TensorFlow), comfort reading and adapting research code.
Soft Skills:
Excellent interpersonal and communication skills.
Ability to work effectively in a team and flexibility;
Proactivity, a strong focus on results, and problem‑solving skills;
Ability to work independently and communicate technical results to a non-specialist audience.
It would be considered a plus:
Familiarity with molecular representations and cheminformatics tools (SMILES, fingerprints, graph neural networks for molecules) or willingness to learn;
Active interest in explainable and interpretable ML (XAI), particularly in applied scientific contexts where trust and transparency are critical;
Hands‑on experience with LLM tool‑use, function calling, or agentic frameworks or conceptual grounding in how LLMs interact with external systems;
Exposure to scientific, industrial, or experimental datasets with inherent noise, class imbalance, or sparse labelling, common in real‑world R&D settings.
What You Will Gain:
Access to a proprietary industrial HPLC dataset not available in academic settings;
AI research challenge with real‑world constraints;
Immersion in cheminformatics and pharmaceutical analytical R&D, with direct collaboration with domain scientists who will challenge and sharpen your modelling decisions;
Research outputs aligned with your doctoral trajectory: publishable methodology, a working system demonstrating scientific AI in industrial settings;
Mentoring from both chemoinformatics and domain experts, with genuine intellectual exchange;
Professional networking opportunities.
Location:
Cambrex Profarmaco Milano Srl, Paullo (MI) On‑site or Hybrid Model (to be discussed)
Contract:
We offer a 1-year scholarship contract; details will be clarified during the interview process.
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