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Postdoctoral In Reinforcement Learning Jobs in Hastings, MN

Varsity Tutors

BCABA Tutor

Edina, MN · Remote

$18 - $40/hr

Ability to explain reinforcement schedules, functional behavior assessment, and behavior ... in learning science to create personalized learning experiences. Through 1-on-1 Online Tutoring ...

Varsity Tutors

BCABA Tutor

Minneapolis, MN · Remote

$18 - $40/hr

Ability to explain reinforcement schedules, functional behavior assessment, and behavior ... in learning science to create personalized learning experiences. Through 1-on-1 Online Tutoring ...

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Postdoctoral In Reinforcement Learning information

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$25.5K

$60.2K

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How much do postdoctoral in reinforcement learning jobs pay per year?

As of Aug 20, 2026, the average yearly pay for postdoctoral in reinforcement learning in Hastings, MN is $60,152.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,900.00 and $67,800.00 per year, depending on experience, location, and employer.

What is a postdoctoral researcher in reinforcement learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in reinforcement learning?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Hastings, MN look for?

The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Hastings, MN are:

What cities near Hastings, MN are hiring for Postdoctoral In Reinforcement Learning jobs?

Cities near Hastings, MN with the most Postdoctoral In Reinforcement Learning job openings:

Postdoctoral In Reinforcement Learning jobs near you
Infographic showing various Postdoctoral In Reinforcement Learning job openings in Hastings, MN as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 27% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $60,152 per year, or $28.9 per hour.

Faculty Positions

University of Minnesota

Minneapolis, MN

$110K - $130K/yr

Full-time

Medical, Dental, Life, Retirement

Re-posted 23 days ago

Job description

About the Job
 

About the Job 

The Department of Electrical and Computer Engineering, University of Minnesota Twin Cities invites applications in the areas of: i) computer engineering, ii) AI, iii) Optics and photonics. These are tenure-track / tenured faculty positions, hiring at the Assistant, Associate, or Full Professor levels; rank and salary will be commensurate with qualifications and experience. 

Computer Engineering:

Expertise in chip design and verification, design automation, computer architecture, and emerging computing platforms is of particular interest, including the fields of AI/ML, heterogeneous integration, quantum, and physics-inspired computing.

Collaboration in research and development in chip design, verification methods, electronic design automation, and computer architecture. Particular application areas of interest include (but are not limited to): AI/ML accelerator design, application of AI/ML for chip design and verification, domain-specific computing, heterogeneous architectures, hardware-algorithm co-design, quantum, and physics-inspired computing.

AI: 

Expertise in algorithms, theory, and applications across the fields of artificial intelligence and machine learning are of particular interest. 

Collaboration in research and development in basic and applied research in artificial intelligence and machine learning. Theoretical areas of interest include (but are not limited to) optimization, neural networks, and reinforcement learning. Application areas of interest include (but are not limited to) robotics, foundation models, image / signal processing, optimization, and control. 

Optics and Photonics:

Expertise in the fields of optics, photonics, nanophotonics, quantum optics, optical materials, devices, instrumentation, and systems including (but not limited to): Integrated and nano-scale photonics; metasurfaces, and plasmonics; Optical manufacturing, patterning, and machining techniques; Biophotonics and optical biosensing; Optical imaging and spectroscopy; Optical measurement and sensing science and instrumentation; Quantum optics; Optoelectronics and silicon photonics; Optical communications and interconnects. 

Collaboration in research and development of one or more of the above listed fields of expertise. Candidates whose work bridges optics and photonics with emerging areas such as quantum technologies or computing, packaging, neuroengineering, biomedical diagnostics, high power devices or systems, or AI-enabled systems are particularly encouraged to apply. Demonstrated ability in micro- and/or nano-fabrication is desirable; candidates are expected to make extensive use of the Minnesota Nano Center (MNC) cleanroom facilities.

Research

  • Develop research proposals for external funding opportunities and collaborate with researchers from academia and industry on joint research projects.
  • Engage in research, publication, and scientific creation.

Teaching

  • Develop and teach undergraduate and graduate courses in the Department of Electrical and Computer Engineering.
  • Advise students regarding specific coursework and broader academic or career goals.

Service

  • Provide service through participation in governance of the department and/or the University.
  • Contribute to outreach activities that support the Department of Electrical and Computer Engineering mission.
  • Contribute to the development of the profession, keep current with the field, and broaden knowledge and expertise.
Qualifications
 

Successful candidates will have outstanding academic and research records and are expected to establish a vigorous, funded research program, teach at the undergraduate and graduate levels, and be involved in service to the University and the profession. An earned doctorate or equivalent foreign degree in an appropriate discipline is required at the time of appointment. It is anticipated that the appointment will begin Fall 2026. Completion of PhD required. 

About the Department
 

The Department of Electrical and Computer Engineering is one of the largest departments within the College of Science and Engineering at the University of Minnesota. Current research areas include Biomedical and Biological Computation Methods; Devices and Systems; Communications, Signal Processing, and Networking; Computer Engineering, VLSI, and Circuits; Fields, Photonics, and Magnetics; Micro and Nano Structures; Sustainable Energy Systems, Power Electronics and Drives; Systems and Controls. To learn more about the Department of Electrical and Computer Engineering at the University of Minnesota, please visit the department website.

Pay and Benefits
 

Pay Range: Assistant Professor $110,000-$130,000, Associate Professor $125,000-$160,000, Full Professor $135,000-$230,000; depending on education/qualifications/experience 

Time Appointment: 100% Appointment

Position Type: Faculty and P&A Staff 

Please visit the Office of Human Resources website for more information regarding benefit eligibility.

The University offers a comprehensive benefits package that includes:

  • Competitive wages, paid holidays, and generous time off
  • Continuous learning opportunities through professional training and degree-seeking programs supported by the Regents Tuition Benefit Program
  • Low-cost medical, dental, and pharmacy plans
  • Healthcare and dependent care flexible spending accounts
  • University HSA contributions
  • Disability and employer-paid life insurance
  • Employee wellbeing program
  • Excellent retirement plans with employer contribution
  • Public Service Loan Forgiveness (PSLF) opportunity
  • Financial counseling services 
  • Employee Assistance Program with eight sessions of counseling at no cost
  • Employee Transit Pass

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