Pioneering AI Scientists Sought for Human Health Revolution in Milan

Human Technopole is seeking ambitious AI Scientists to develop sophisticated AI systems aimed at deciphering human disease mechanisms through population-scale genetic and biomedical data analysis.

Advancing Biomedical Discovery Through AI Innovation

Milan, Italy, is rapidly emerging as a central hub for life science innovation, with the Human Technopole institute leading the charge in biomedical research. This dynamic organization is actively seeking highly skilled and ambitious AI Scientists to join its mission. The primary objective is to harness the power of artificial intelligence to unravel the complexities of human diseases, leveraging vast datasets encompassing genetics, molecular biology, and clinical information. This initiative is not merely about applying existing AI tools, but about forging new pathways in AI methodology, creating intelligent systems that can fundamentally alter how we understand and combat illness.

The institute, characterized by its international research environment and state-of-the-art technological infrastructure, aims to translate bold scientific concepts into tangible advancements that directly improve human health outcomes. Central to this endeavor is the Casale Group, situated within Human Technopole's Health Data Science Centre. This group specializes in developing AI methods that convert large-scale human data into mechanistic models of disease, generating testable hypotheses that can accelerate biological discovery and drive therapeutic innovation. The current recruitment drive for AI Scientists is designed to bolster this capacity, bringing in individuals capable of constructing the foundational AI architectures and agentic scientific systems necessary to power the group's diverse research projects across human genetics, multimodal biology, and disease.

The Role of the AI Scientist: Bridging Biology and Advanced AI

The position of AI Scientist, or Senior AI Scientist, at Human Technopole offers a unique opportunity to work at the cutting edge of biological research and artificial intelligence. The successful candidates will be tasked with developing next-generation AI systems specifically designed to analyze population-scale genetics and multimodal biomedical data. The ultimate goal is to uncover novel mechanisms underlying human diseases, pushing the boundaries of what is currently understood.

Key responsibilities include transforming research methodologies and models into robust, reusable AI capabilities. This involves the creation of foundation models, sophisticated multimodal representation learning approaches, and agentic workflows. Furthermore, the role demands the development of comprehensive evaluation frameworks and scalable research software. These tools are crucial for accelerating discovery across a multitude of interconnected projects within the institute.

Looking further ahead, a significant aspiration for this role is to develop AI systems that function as genuine scientific collaborators. Imagine AI that can connect evidence across different biological scales, propose insightful hypotheses, identify the most informative analyses, and work synergistically with human researchers to expedite iterative cycles of biological discovery. This vision requires a unique blend of methodological innovation, deep biological insight, and expert scientific software development, all at the intersection of artificial intelligence, computational biology, and human genetics. Successful candidates will enjoy substantial scientific and technical ownership, with the autonomy to shape the group’s technical AI architecture while contributing to high-impact scientific publications, open-source software initiatives, and the creation of reusable research capabilities.

A Collaborative Ecosystem for Groundbreaking Research

Human Technopole fosters a highly interdisciplinary AI ecosystem, providing a fertile ground for collaboration and knowledge exchange. AI Scientists will actively engage with the broader European AI community, including through strategic partnerships with institutions like Helmholtz Munich and connections to the renowned ELLIS network. This expansive network ensures that researchers are at the forefront of AI advancements and can contribute meaningfully to its development.

The institute’s collaborative environment extends internally, with extensive opportunities for interaction with computational, biological, and clinical groups across the campus. Beyond national borders, international collaborations, research exchanges, interdisciplinary training programs, and engagement with industry partners further enrich the scientific experience. The Casale Group, in particular, benefits from an established network of leading academic institutions and industry partners throughout Europe and North America, offering invaluable opportunities for building collaborations, broadening expertise, and accelerating the impact of research findings.

This supportive framework is designed to empower AI Scientists to enhance their scientific and professional skills continuously. They will develop next-generation AI methods through ambitious biological research, build advanced models for scientific discovery, and transform scientific breakthroughs into robust, benchmarked, and reusable AI capabilities. The role also involves building scalable research software and AI workflows for managing large and heterogeneous scientific datasets, thereby shaping the future of AI for science through impactful publications, open-source contributions, international partnerships, and entrepreneurial ventures. Human Technopole is committed to supporting career development through dedicated training programs, mentorship, and continuous learning opportunities.

Ideal Candidate Profile and Application Details

Human Technopole is seeking candidates with a strong academic foundation and practical experience. Essential qualifications include a PhD in Machine Learning, Artificial Intelligence, Computer Science, Computational Biology, or a closely related quantitative discipline, or the expectation of completing one within the next six months. Applicants should possess at least four years of relevant research experience, encompassing their doctoral research. A demonstrable track record of scientific output in AI, machine learning, computational biology, or biomedicine is critical, evidenced by independent methodological or technical contributions through publications, open-source software, models, or similar research products.

Proficiency in Python programming and software engineering is a prerequisite, with proven experience in developing scalable and maintainable research software, model-training workflows, or AI systems. Candidates must exhibit strong expertise in modern machine learning and AI, coupled with the ability to design, develop, and critically evaluate advanced methods for complex research problems. Experience with contemporary software engineering practices, such as version control, testing, documentation, reproducibility, and working within large-scale GPU or high-performance computing (HPC) environments, is also necessary.

Preferred qualifications include industry or applied research and development experience, particularly in roles like AI/ML architect, research scientist, or machine learning scientist, where significant technical ownership was held. Experience in developing foundation models or other large-scale AI systems for scientific or biomedical applications is highly valued. The institute is also interested in candidates with experience developing or applying Large Language Model (LLM)-based agents and agentic workflows for scientific or technical tasks, including analysis, information integration, automation, or hypothesis generation. Experience in integrating multimodal biomedical datasets (e.g., molecular, imaging, clinical, population-scale data), leading collaborative software projects, and contributing to open-source scientific software or models is a plus. Familiarity with workflow management, containerization, and scalable GPU/HPC computing is beneficial. While not strictly required, an understanding of human genetics and computational biology, or experience applying AI within drug discovery, translational research, or related biomedical pipelines, would be advantageous.

Beyond technical skills, the institute values strong analytical thinking, scientific curiosity, and the ability to work independently while thriving in an interdisciplinary and collaborative setting. Excellent communication and scientific writing skills are essential, alongside creativity, intellectual humility, and a genuine enthusiasm for tackling challenging biological questions. Proactivity, self-motivation, and eagerness to take ownership of ambitious research projects are key attributes for success.

The application closing date for this role is November 11, 2026, with a planned start date of January 11, 2027, though a later commencement can be arranged. The position is a four-year contract, offered under the CCNL Chimico Farmaceutico, Level B1, with a competitive salary up to €58,000 depending on seniority. The role is based in Milan, Italy, within Human Technopole’s vibrant international campus. The institute encourages applications from diverse backgrounds, providing an international and dynamic workplace, competitive welfare provisions, flexible working policies, and relocation support. Those moving to Italy may also benefit from attractive tax incentives, alongside initiatives promoting work-life balance and parental support. More details can be found on Nature Careers, where the original advertisement was posted.

Why it matters

The global push to integrate AI into foundational scientific research, particularly in areas as critical as human health, represents a paradigm shift for many sectors. For data center operations, this translates to an increasing demand for specialized compute infrastructure capable of handling massive, complex datasets and computationally intensive AI models, including foundation models and multimodal systems. Telco providers will see rising traffic from data transfer between research institutions, cloud-based AI services, and distributed computing resources. In-field AI applications, such as augmented reality tools for surgical planning or diagnostic assistance, will directly benefit from the mechanistic disease models and enhanced understanding generated by this research, potentially leading to more precise and personalized medical interventions. Technicians supporting these advanced scientific endeavors will require specialized skills in maintaining and optimizing high-performance computing clusters, GPU farms, and secure data storage solutions crucial for processing sensitive genomic and biomedical data. The development of reusable AI capabilities and scientific collaboration platforms, as envisioned by Human Technopole, also highlights a future where AI tools empower a broader range of scientific and technical professionals, democratizing access to advanced analytical methods and accelerating innovation across multiple industries, including biotech, pharma, and healthcare IT infrastructure development.