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