Our group has a strong focus on the discovery of how different types of health data (clinical, systems biology, genomics, microarrays) can help us diagnose, prognose and ultimately treat different diseases using existing interventions or novel treatment/drug repurposing strategies. We use latest tools and methods of machine learning including deep learning, explainable AI, multi-task learning, transfer learning, reinforcement learning and so on. We incorporate the latest knowledge in clinical informatics and molecular biology with the state-of-the-art and novel informatics approaches to explore disease onset and evolution using extensive clinical and biological informatics tools and databases. We ground our research through close collaborations with clinicians, medical researchers, industry and researchers from different areas (bioinformatics, health informatics, industrial and systems engineering, statistics and computer science) to ensure that our research is applicable in the real world.
The goal of the PhD projects will be to contribute to one or more of the existing research areas in translating health informatics data to actionable diagnostic, prognostic and treatment information while developing novel computational (machine learning, statistical, and/or optimization) approaches. The assistantship recipient will be able to work in collaboration with clinicians, industry and other researchers.
Examples of projects could be: • Developing novel computational and analytic models and methods to identify cancer patient subtypes with the ultimate purpose of personalizing treatment strategies by integrating multiple data types (such as gene expression, somatic mutation, copy number variation, and clinical data). • Explore how similarities in different cancers’ molecular biology can be leveraged to improve diagnostic and prognostic biomarkers and for drug repurposing. • Develop a data-driven computational model capable of learning the abnormal and lethal brain anatomy through neonatal MRIs to detect total and regional neuroanatomical deviations. • Developing computational models and methods to integrate learning task specific parameters and latent factors using health informatics dataset from disparate cohorts and different studies.
Qualifications In terms of education, candidates must have a Master (or equivalent) degree in computer science or a related discipline (e.g., statistics, computational mathematics, industrial engineering, operations research or management science with a specialization in data science and machine learning) and eager to become a data science and analytics researcher.
Ideally, the candidate should have some of the following qualifications:
• Strong analytical skills (e.g. statistics, probability, mathematical modeling) and programming skills in one or more languages (R, Matlab, Python, C++).
• Desired but not required qualifications are interest and knowledge in healthcare applications and publications (conference proceeding or journal article).
• Belong to the top of your graduating class as evidenced by your grades and supported by your references.
• Self-motivated, fast learner, dedicated, autonomous and creative.
• Genuine interest (or experience) in predictive analytics (data mining, machine learning, artificial intelligence) and willing to demonstrate this as part of the application process.
• Excellent analytical skills and willing to implement your ideas in software.
• Ambitious, but at the same time a team player.
• Excellent communication skills.
Admission and Enrollment Information: The scholarships for the PhD degree are subject to academic approval, and the candidates will be enrolled in the Computer Science PhD program. For information about our enrollment requirements and the general planning of the PhD program, please see the WSU and CS Department PhD Guide.
• A fully funded position with a highly competitive salary
• Working in a scientifically stimulating, innovative, dynamic, well- equipped environment
• Opportunity to work closely with cross-disciplinary academic, healthcare, and industry partners
• State-of-the-art research facilities and computational equipment
• Excellent support for post-graduation employment opportunities in academia, industry and government.
About Department of Computer Science & Wayne State University: The Department of Computer Science has 21 tenured or tenure-track faculty members, with research strengths in the areas of Artificial Intelligence, Bioinformatics, Computer Systems, Data Mining, Graphics and Visualization, Software Engineering, and Wireless Networking. The research in the department is also highly interdisciplinary with active collaborations with faculty in medicine, engineering, and sciences and with local industry such as the Ford Motor Company and General Motors. The Department has five NSF CAREER recipients and an external funding of around $2.5M annually. The department currently has 75 Ph.D. students, along with 100 Master's students, and 650 undergraduate majors. Further information about the department can be found at http://www.cs.wayne.edu. The Department of Computer Science is in the College of Engineering. The College has an annual research expenditure of about $20M. Information about the College of Engineering can be found at http://engineering.wayne.edu. Wayne State University is classified by the Carnegie Foundation for the Advancement of Teaching as RU/VH (Research University, Very High research activity), a distinction held by only 3.5 percent of institutions of higher education in the United States. Wayne State University, in partnership with the University of Michigan and Michigan State University, has a key role in Michigan's University Research Corridor initiative (http://urcmich.org/) and is closely involved with TechTown, the area's business incubator (http://techtownwsu.org/). Located in the center of the U.S. automotive industry (e.g., within ~20 miles from the research centers of Ford and GM), with the largest single-campus medical school in U.S., and being a leading force in the revitalization of the City of Detroit, Wayne State University serves as an excellent campus for pursuing transformative research, education, and service. Besides enjoying the beautiful fall scenery as well as winter skiing in Michigan, within a 20-mile radius.
Application: For prioritized consideration please email your application no later than 23 April 2021 to Dr. Suzan Arslanturk at email@example.com. Applications and enclosures received after the deadline will be considered only if the position is not filled.
Applications should include the following documents (ideally combined into one pdf document): • Cover letter (including a brief description of past related experience and future interests, as well as the earliest possible starting date)
• A detailed Curriculum Vitae
• Grade transcripts listing course titles for all degrees (unofficial transcripts accepted)
• Scholarly publications including journal articles, conference proceedings, technical reports, theses, term papers, etc.
• Name and contact details of at least two referees
Candidates may apply prior to obtaining their master's degree but cannot begin before having received it. All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply.