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Postdoctoral In Reinforcement Learning Jobs in Texas

Apple

Machine Learning Engineer

Austin, TX · On-site

Skilled in communication, problem solving, critical thinking.Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus.Experience ...

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Postdoctoral In Reinforcement Learning information

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 are the key skills and qualifications needed to thrive as a Postdoctoral Researcher in Reinforcement Learning, and why are they important?

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 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.
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What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Texas look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Texas are:
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Postdoctoral In Reinforcement Learning jobs near you
Postdoctoral Associate - cancer epidemiology

Postdoctoral Associate - cancer epidemiology

Baylor College of Medicine

Houston, TX • On-site

$62K/yr

Full-time

Posted 8 days ago

Baylor College of Medicine rating

8.6

Company rating: 8.6 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

50th of 532 rated colleges and universities

Job description

Job Title: Postdoctoral Associate - cancer epidemiology
Division: Medicine
Work Arrangement: Onsite only
Location: Houston, TX
Salary Range: $62,232
FLSA Status: Exempt
Work Schedule: Monday - Friday, 8 a.m. - 5 p.m.
Summary
The Section of Epidemiology and Population Sciences at Baylor College of Medicine invites applications for Postdoctoral Associate position in systems epidemiology of cancer.
The training provides epidemiology and bioinformatics Postdoctoral Associate with the specialized skills to incorporate novel high-throughput technologies into large-scale collaborative epidemiology studies and to become successful, cross-trained researchers. Exciting opportunities also exist to work with faculty from MD Anderson Cancer Center, Rice University and UTHealth School of Biomedical Informatics.
Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.
Job Duties
  • Analyzes large data sets.
  • Analyzes next generation sequencing data (e.g., RNA-seq, whole genome/ exome sequencing) to address complex biological and translational research questions.
  • Uses state-of-the-art bioinformatical and statistical tools.
  • Develops new statistical methods to answer biological questions that arise in the research.
  • Prepares manuscripts, abstracts, and presentations for peer-reviewed journals and scientific conferences.
  • Ensures compliance with institutional, sponsor and data security guidelines for handling sensitive genomic and clinical data.
  • Conducts advanced analysis of large-scale biomedical and population health datasets to support ongoing research within the section.
  • Collaborates with multidisciplinary teams that includes faculty investigators, statisticians, clinicians, and trainees, in a highly team-oriented research environment.
  • Participates in the Section's sponsored training program by contributing to collaborative projects, mentoring trainees, and engaging in programmatic activities such as seminars, workshops, and collaborative research initiatives.
  • Develops methodologies and tools for use in the electronic medical records that will strengthen the Learn Health System and improve patient outcomes.
  • Performs other job-related duties as assigned.

Minimum Qualifications
  • MD or Ph.D. in Basic Science, Health Science, or a related field.
  • No experience required.

Preferred Qualifications
  • Ph.D. in Epidemiology or Bioinformatics or related field.
  • Prior experience in analysis with real-world data, high-dimensional data, causal inference, and/or machine learning /artificial intelligence in a plus.
  • Knowledge in cancer biology or genetics is desired.
  • Excellent writing skills in English.
  • Ability to work in a team environment.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.
PD; SN
Requisition ID: 25324

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