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PhD in the field of Computer Graphics, Computer Science, Mathematics or Machine Learning with a proven academic or equivalent industrial track-record. The position requires a combination of education and experience in computer vision, computer graphics, robotics and machine learning.
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Stay current with the latest advancements in image processing, computer vision, and machine learning technologies, and apply cutting-edge techniques to solve challenges in NGS.
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MS or Ph. D. degree in biomedical engineering, physics, computational sciences, or a closely related field with emphasis on medical image processing. Strong understanding of data science approaches including machine and deep learning; background in immunology, biology or oncology a plus.
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We apply advanced technologies such as Digital Signal Processing, Analytics, Machine Learning, Artificial Intelligence, and Cloud computing to solve our customer and mission challenges.
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Bachelor’s degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field with 6+ years of professional experience in data science/machine learning.
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The lab is seeking a Science and Engineering Associate 2 to perform advanced computational engineering tasks in support of the VMR with the goal of expanding the database and making it compatible with research tasks in machine learning (ML) and artificial intelligence (AI.
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BSc/MSc graduate in the field of engineering, computer science, applied mathematics, physics, statistics, or relevant field of study. Develop and implement novel AI machine learning algorithms in the area of Large Language Models (LLMs) and/or Computer Vision models to provide automation of clinical tasks using one or more of data domains (e.g. medical images, electronic medical records, clinical reports, etc.
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The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch.
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Prototyping and engineering experience in at least ONE relevant specialization area in either Computer Vision or Machine Learning: SLAM State Estimation, Online Calibration, Sensor Fusion, 3D reconstruction, object detection, segmentation and tracking, scene understanding, or semantic segmentation.
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Master's or Doctoral degree in Computer Science (with a focus on Data Mining, Machine Learning), Statistics, Econometrics, Operational Research, or other rigorous quantitative disciplines that require processing and modeling data at a complex and large scale.
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At least 5-10 years’ directly related experience in computer applications, large scale computing frameworks, data analysis systems, machine learning techniques, data engineering, and pipeline design for clients for real world problems.
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Transform your career to the next level with GDIT as a Solutions Architect where you can elevate your skills in Data Science, Machine Learning, or leading AI techniques and technology expertise into solutions in support of our Government customers.
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Apply classical and machine learning methods to derive insights from integrating and analyzing clinical response, tumor transcriptomic/genetic data, and radiographic image features.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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This is an excellent opportunity to shape the future of AI-driven protein design and to work cross-functionally with a diverse team of experts across machine learning, protein engineering, cell biology, and gene editing.
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machine learning computer science image processing engineering jobs Company: Delta Industrial Services Inc in Anoka, Minnesota
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Virtual Reality Job Interviews
With the advent of desktop computers, the arduous task of scouring through weekly job classifieds became a thing of the past. The mid-1990s brought about a new era where job seekers could easily search and apply for jobs online. The introduction of AOL's Instant Messaging feature provided an even faster means for employers and candidates to communicate and schedule interviews. As smartphones became more pervasive in the early 2000s, hiring managers increasingly used phone calls for screening and interviewing candidates. Despite this trend, over 80% of interviews still took place in person.
A Potential TikTok Ban?!
As you may already know, there has been a lot of talk lately about the possibility of a TikTok ban. While this has not yet come to fruition, it's important to consider the implications this could have for businesses and recruiters who rely on TikTok as a platform to market their brand, recruit new talent, and connect with their audience.
The Effects of Workplace Racism and Sexism
One day it's a covert statement to a mother returning to work after maternity leave. Another day it's a lingering gaze at an employee enjoying a culturally rich meal. These microaggressions (or sometimes macroaggressions) can take an employee from a confident, high-performer to one that feels insecure being themselves at work. Your employees engage with people with different ideas and feel most comfortable and valued when they can work without losing their cultural, racial, and gender identity. While most employers know this, why have workplace racism and sexism often been neglected?
When Rage Applying Strikes: How to Identify Unserious Candidates
As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
How to Increase Job Ad Exposure
In today's competitive job market, writing quality job ads is critical for attracting top talent to your organization. While networking and candidate referrals are prime real estate for finding qualified candidates, nothing beats the tried-and-true method of writing an extraordinary job ad. But while writing a great job ad is the first step, what's more important is increasing visibility. You could have the most detailed, well-written ad on the internet, but if no one sees it, then you are wasting time (and potentially money!). Employers often believe that job boards are the root of the problem, but you can learn how to increase job ad exposure by tweaking a few steps of your recruitment process.
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The job market has shifted significantly in recent years. The accelerated adoption of technology has not only pushed many companies into remote working arrangements but also increased the availability of supporting tools and technologies (i.e., video conferencing and collaboration software).