Faculty Position (Open Rank) in Department of Machine Learning

H. Lee Moffitt Cancer Center

Tampa, FL

Job posting number: #7069636 (Ref:hlj_27173)

Posted: September 10, 2020

Application Deadline: Open Until Filled

Job Description

The newly established Department of Machine Learning (ML) at Moffitt Cancer Center, a National Cancer Institute-designated Comprehensive Cancer Center, is seeking new faculty members in the tenure-earning rank of Assistant/Associate Member with research interests in artificial intelligence, machine and deep learning and their application in cancer discovery and clinical care. 

Position Highlights: 
The new faculty will join a recently established and expanding Department, which is a first of its kind in oncology.  We currently have faculty initiating a wide range of machine learning research and its application in oncology in collaboration with other members of the Quantitative Science Division, including the Biostatistics and Bioinformatics Department and the Integrated Mathematical Oncology Department, as well as with other research and clinical departments within the cancer center. The new faculty will have access to extensive retrospective data for real-world predictive analytics, and other clinical research resources including an integrated repository of clinical, genomic, imaging and patient-reported information as well as biospecimens from a large cohort of patients.  Moffitt also enjoys a very active clinical and radiological translational research program investigating a wide variety of novel therapeutics in translational and basic sciences, with an emphasis on health outcomes, epidemiology, immunology, cancer biology and evolution, and drug discovery. 

Numerous research opportunities are available to reduce cancer burden, including areas with reaserch emphasis on  on large-scale clinical, patient-reported, and wearable sensor data to identify patients at risk of acute treatment toxicities and poor outcomes.  Collaboration with implementation scientists offers the opportunity to integrate machine learning algorithms into the electronic medical record and clinic workflows to improve clinical care.  

The Center is characterized by a culture of collegiality and team science, facilitating cross-disciplinary collaborations.  Faculty development is a tenant of Moffitt culture and an essential part of this new department’s philosophy to develop future leaders in the emerging field of machine learning in oncology. In addition to Moffitt’s extensive computational and rich data resources, the ML Department features state of art DGX-A100 cluster for advanced machine learning applications with retrospective and prospective comprehensive clinical, imaging, and molecular datasets. 

The Ideal Candidate:
•    We seek candidates with expertise in artificial intelligence, machine and deep learning who are interested in applying this expertise to cancer research and translational oncology. Candidates will contribute to current and initiate future machine learning applications at Moffitt, as evidenced by a history of peer-reviewed publications and involvement in grant supported research projects. Ongoing research includes but is not limited to outcome modeling, human-machine interaction, clinical decision support, information retrieval, quantitative imaging (radiomics), digital pathology (pathomics), and computational biology, among others where machine learning can accelerate cancer discovery and improve care delivery. 
•    Preference will be given to applicants with an outstanding record conducting team science or collaborative research with an emphasis on machine learning in healthcare. Areas of particular interest include: application of deep learning, natural language processing, explainability and interpretability of machine learning in outcome modeling, human-machine interaction, clinical decision support, and information retrieval.
•    Demonstrate experience (or potential) as a collaborative or independent researcher with extramurally funded research studies, presentations at national and international conferences, and a record of high-quality peer-reviewed publications. 

Responsibilities: 
•    Maintain a productive integrated and/or independent research program in machine learning in oncology.
•    Collaborate on a variety of machine learning research projects both within Moffitt and externally. 
•    Engage in educational (e.g., mentorship) and service activities across Moffitt and its affiliates 

Credentials and Qualifications: 
•    Doctorate degree in computer science, engineering, mathethmatics, statistics or releveant field with appropriate research training and experience.
 
Academic rank beyond Assistant Member will be commensurate with experience and qualifications. Moffitt is affiliated with the University of South Florida and a University appointment is available in the rank of Assistant/Associate Professor as applicable in the appropriate departments. Moffitt-based faculty members focus their efforts on research, with minimal expectations for formal teaching.  

Salary and start-up funds are highly competitive. 

Tampa was named an “urban gem” and the “Best City in the Southeast” by Money magazine.  Aside from the sunshine, beaches, and year-round outdoor recreation associated with the Gulf Coast of Florida, Tampa is a thriving metropolitan city with unique cultural attractions, low cost of living, and a high quality of life. 

Questions regarding the position should be directed to: Issam El Naqa, PhD, Search Committee Chair, Department of Machine Learning (Issam.Elnaqa@moffitt.org). 
 



Mission To create a Moffitt culture of diversity and inclusion as we strive to contribute to the prevention and cure of cancer. Vision Moffitt Cancer Center is recognized as the model wherein the diversity of our employees and communities is valued and supported as essential components to contributing to the prevention and cure of cancer. The cancer center is an equal opportunity employer. It is the policy of the cancer center to prohibit unlawful discrimination and harassment of any type and to afford equal employment opportunities to workforce members and applicants, regardless of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, marital status, disability, genetic information, veteran’s status or any other characteristic protected by federal, state or local law.


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