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Phd Computer Science Jobs in Plainfield, NJ (NOW HIRING)

The ideal candidate will have a PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field and a strong background in designing and building AI-driven systems. This role ...

The ideal candidate will have a PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field and a strong background in designing and building AI-driven systems. This role ...

Showing results 21-40

Phd Computer Science information

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$58K

$85.3K

$100.5K

How much do phd computer science jobs pay per year?

As of Sep 1, 2026, the average yearly pay for phd computer science in Plainfield, NJ is $85,253.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,500.00 and $95,900.00 per year, depending on experience, location, and employer.

What is a PhD in computer science?

A PhD in Computer Science is the highest academic degree in the field, focused on advanced research and the creation of new knowledge in computing. It typically involves several years of coursework followed by original research culminating in a dissertation. Graduates often pursue careers in academia, research, or advanced industry roles that require deep technical expertise and problem-solving skills.

What are the key skills and qualifications needed to thrive as a PhD in computer science?

To thrive as a PhD in Computer Science, you need advanced expertise in algorithms, programming, and research methodologies, typically supported by a doctoral degree in computer science or a related field. Mastery of programming languages (such as Python, Java, or C++), data analysis tools, and familiarity with version control systems like Git are commonly required, along with experience in publishing academic research. Critical thinking, problem-solving, strong written and verbal communication, and perseverance are vital soft skills for success in research and collaboration. These skills and qualifications are essential for making significant contributions to the field, driving innovation, and effectively sharing knowledge with the academic and professional community.

What are some common challenges faced by PhD computer science students during their research?

PhD Computer Science students often encounter challenges such as defining a clear and impactful research problem, managing long-term projects with limited guidance, and coping with the pressure to publish in top-tier conferences or journals. Balancing coursework, teaching responsibilities, and research can also be demanding. Effective time management, networking with peers and mentors, and seeking regular feedback can help students navigate these challenges and achieve their academic goals.

Is a PhD worth it for computer science?

A PhD in computer science can lead to careers in research, academia, or specialized industry roles, often requiring advanced skills in algorithms, data analysis, and programming. While it offers opportunities for high-level positions and expertise, it typically involves several years of study and may not be necessary for most industry jobs, which often value practical experience and skills. The decision depends on career goals and the desire for research or teaching roles.

What can I do after a PhD in computer science?

A PhD in computer science prepares individuals for careers in academia, research, or advanced industry roles such as data scientist, machine learning engineer, or software architect. Graduates often pursue postdoctoral research, work in R&D departments, or obtain certifications in specialized tools and programming languages to enhance their expertise.

What jobs can I get with a PhD in computer science?

A PhD in computer science qualifies individuals for advanced roles such as research scientist, data scientist, machine learning engineer, or university professor. These positions often require strong analytical skills, programming expertise, and knowledge of algorithms, data structures, and AI tools. Graduates may work in academia, industry research labs, or technology companies focusing on innovation and development.

What are popular job titles related to Phd Computer Science jobs in Plainfield, NJ?

For Phd Computer Science jobs in Plainfield, NJ, the most frequently searched job titles are:

What job categories do people searching Phd Computer Science jobs in Plainfield, NJ look for?

The top searched job categories for Phd Computer Science jobs in Plainfield, NJ are:

What cities near Plainfield, NJ are hiring for Phd Computer Science jobs?

Cities near Plainfield, NJ with the most Phd Computer Science job openings:

Infographic showing various Phd Computer Science job openings in Plainfield, NJ as of August 2026, with employment types broken down into 1% As Needed, 79% Full Time, 17% Part Time, 2% Contract, and 1% Nights. Highlights an 73% Physical, 2% Hybrid, and 25% Remote job distribution, with an average salary of $85,253 per year, or $41 per hour.

Research Scientist, Artificial Intelligence (PhD)

Synaptrix Labs

New York, NY

$85K - $150K/yr

Full-time

PTO

Re-posted 28 days ago


Job description

About Synaptrix Labs Inc.

Synaptrix is on a mission to revolutionize brain-computer interfaces through non-invasive approaches. We believe that the power to diagnose and treat neurological conditions safely, and to expand human potential, will become a reality with the right fusion of deep learning, signal processing, and computational neuroscience.

We're seeking a full time Research Scientist, Artificial Intelligence (PhD) to join our growing team of researchers & engineers. If you're passionate about shaping the future of brain-computer interfaces and excited by the potential of deep learning in neurotechnology, we want to hear from you!

Responsibilities:

  • Design, prototype, and optimize state-of-the-art AI systems for neural decoding, including diffusion models, graph neural networks, contrastive/self-supervised frameworks, and transformer-based sequence models.
  • Conduct foundational research on neural time-series representation learning: build architectures that extract latent dynamics from EEG, EMG, or related biosignals.
  • Develop high-fidelity simulation environments for testing decoding algorithms, incorporating stochastic signal noise and realistic biophysical constraints.
  • Scale model training across multi-GPU and multi-node clusters using PyTorch Distributed, DeepSpeed, or JAX/Flax; profile and tune system performance for sub-10 ms inference latency.
  • Build and maintain end-to-end research pipelines for large-scale signal datasets, including preprocessing, artifact rejection, and multimodal fusion with video, audio, and IMU data.
  • Collaborate with neuroscientists and hardware engineers to integrate learned models into real-time BCI control loops and embedded systems.
  • Contribute to core ML infrastructure: experiment tracking, model versioning, dataset lineage, and reproducibility standards.
  • Publish at top-tier ML or neurotech venues (NeurIPS, ICLR, Nature Neuro, EMBC) and present findings to the research community.

Minimum Qualifications:

  • PhD or equivalent deep technical expertise in Machine Learning, Artificial Intelligence, Computer Science, Computational Neuroscience, or related fields.
  • Strong command of PyTorch or JAX, with experience implementing custom training loops, loss functions, and model architectures.
  • Proven ability to conduct end-to-end research, from conceptual design to reproducible experiments and evaluation.
  • Strong mathematical foundations in linear algebra, probability, optimization, and information theory.
  • Experience working with high-dimensional time-series or sensory data (EEG, speech, video, motion capture, etc.).
  • Skilled in Python, NumPy, Pandas, and scientific computing workflows; experience with CUDA or low-level GPU debugging is highly valued.
  • Demonstrated ability to operate independently on open-ended problems and drive original research with limited supervision.

Preferred Qualifications:

  • Deep familiarity with neural signal modeling, neural decoding, or biosignal preprocessing (EEG/MEG/ECoG/EMG).
  • Experience designing self-supervised or generative models (diffusion, VAEs, contrastive, masked modeling) for noisy, non-stationary data.
  • Background in reinforcement learning, optimal control, or human-in-the-loop systems, especially in continuous domains.
  • Publications or preprints in top venues (NeurIPS, ICML, ICLR, CVPR, EMBC, Nature Neuro).
  • Familiarity with distributed training, mixed-precision, multi-GPU orchestration, and cloud ML infrastructure (AWS/GCP/Azure).
  • Contributions to open-source ML frameworks or custom CUDA kernels.
  • Understanding of neural signal acquisition hardware, embedded inference, or edge ML deployment.
  • Track record of curiosity-driven, independent research resulting in practical systems or open-source codebases.

About our Culture:

At Synaptrix Labs, we celebrate curiosity, open collaboration, and scientific rigor. Our interdisciplinary team spans neuroscience, AI, and clinical research, and we are united by the belief that non-invasive BCI is the key to unlocking a new era in healthcare, accessibility, and human augmentation.

Expected Compensation:

The base salary for this role is anticipated to fall within the following range. Actual compensation will depend on your experience, technical expertise, and relevant education or training. In addition to base pay, Synaptrix offers equity to all full-time employees, reflecting our commitment to shared success and long-term company growth.

Base Salary Range:

$85,000 - $150,000 USD

What We Offer:

  • An opportunity to change the world and work with some of the smartest and most talented experts from different fields
  • Growth potential; we rapidly advance team members who have an outsized impact
  • Paid holidays, unlimited PTO