PhD in Artificial Intelligence in Medicine - Hiring in process/Finished, not possible to apply

Research project and group

A vast collection of biomedical "big data" is available in individual laboratories and public repositories, e.g. large datasets of images from drug and genetic screens, large collections of biomedical text and genomic, proteomic and epidemiologic datasets. Such data can potentially be used to define biological signalling pathways, discover new disease links and develop new therapies. Despite this potential, the full richness of this data is not yet exploited because it is challenging to analyze such complex information.

In recent years, great advancements have been made in artificial intelligence, e.g in deep learning and transfer learning. The “Cell Death and Lysosomes” group makes use of this by developing artificial intelligence-based methods for analysing and integrating different types of biomedical big data, including images, text and tabular data.  This is done in close collaboration with other groups from medicine, mathematics and computer sciences.

The group has access to supercomputing infrastructure and is part of a vibrant international and interdisciplinary research community. It is connected to the large AI Lund network, comprising academic groups and industry working with artificial intelligence, the artificial intelligence working group at the Lund Institute for Advanced Neutron and X-Ray Science (LINXS), and several other local and international networks. The student will participate in activities of these networks and of the COMPUTE Research School. A broad range of additional career development activities are available and the group prides itself in the competency development of its students.

Work duties

Main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects of the Cell Death and Lysosomes Group and third cycle courses.

Research will primarily entail:

• development of artificial intelligence methods for analysing different types of biomedical data
• comparison to current analysis methods
• application of developed methods to studying cell death
• development of strategies and tools for annotating, storing and visualizing multi-parametric biomedical "big data"

Some travel within Sweden or internationally can occur on a voluntary basis.

Like all members of the Cell Death and Lysosomes Group, students will also define an individual career development plan together with supervisors and take part in appropriate competence development activities.

Requirements

• The applicant should have a university education within computer science, mathematics, engineering, natural sciences, medicine, biomedicine or related subjects or acquired corresponding knowledge in other ways.
• Independence, ability to develop his/her own ideas and solve biomedical and interdisciplinary problems
• Ability to work with others, communicate
• Express him/herself in English
• Organizing and planning ability 
• Curiosity, creativity, engagement and intelligence

Additional requirements

• Knowledge within programming, artificial intelligence/machine learning, natural language processing, image analysis or analysis of Big Data will be regarded as particular merits

Eligibility
Students with basic eligibility for third-cycle studies are those who- have completed a second-cycle degree- have completed courses of at least 240 credits, of which at least 60 credits are from second-cycle courses, or- have acquired largely equivalent knowledge in some other way, in Sweden or abroad.

The employment of doctoral students is regulated in the Swedish Code of Statues 1998: 80. Only those who are or have been admitted to PhD-studies may be appointed to doctoral studentships. When an appointment to a doctoral studentship is made, the ability of the student to benefit from PhD-studies shall primarily be taken into account. In addition to devoting themselves to their studies, those appointed to doctoral studentships may be required to work with educational tasks, research and administration, in accordance with specific regulations in the ordinance.

Type of employment
Limit of tenure, four years according to HF 5 kap 7§.

Instructions on how to apply

Applications shall be written in English and include:

• 1-page cover letter stating why you are interested in the position, how the research project matches your interests and skills, and how you would be of benefit to the group
• concise CV listing in the following order (leave out non-applicable sections): 1. current contact details, 2. formal education, 3. other training (online courses, etc), 4. research/work experience, 5. other relevant experience (e.g. international stays, conference participation, volunteering), 6. competence level in programming languages, 7. other relevant skills (especially skills related to artificial intelligence/machine learning, natural language processing, image analysis or analysis of Big Data or biomedical data), 8. links to work samples and work-related online profiles (e.g. LinkedIn, ResearchGate, GitHub), 9. publications and 10. two references who have agreed to be contacted
• degree certificate
• grade transcripts (including note about the scale)
• other material you wish to be considered (e.g. recommendation letters)