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Thijs B.

@tbruinsslot

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5.0
1

3.7

3.7

100%

Python Developer | Machine Learning | Biomedical

$45 USD / Hour

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Netherlands (9:02 PM)

Joined on September 2, 2019

$45 USD / Hour

⭐⭐⭐⭐⭐ I have worked closely with Thijs for more than 5 years and was consistently impressed by the quality of his work. As a Python software engineer, Thijs delivered high-quality code with efficiency and precision, which significantly contributed to the success of our FEops HEARTguide technology. What truly differentiates Thijs is his broad set of technical skills, and his ability to quickly learn new things. Besides Python he also has expertise in medical image analysis and computational modelling. - Peter Mortier, CTO at FEops As an experienced biomedical engineer, I specialize in advanced medical imaging modalities and diagnostics. With over 5 years of experience as a software developer in the medical sector, I've focused on image processing, AI algorithm design, and FDA-approved medical device applications. My expertise extends to coding, computer vision, 3D graphics, and deep learning. Embracing the flexibility and diversity of freelance work, I offer a unique blend of technical proficiency and adaptability. Explore my capabilities and previous projects at [login to view URL].

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On time

100%

On budget

100%

Portfolio

Portfolio

5796284
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5796284
11761416
11761513
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11761488
5640087
5.0 · 1 Review
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Phuong V.

5.0

$700 USD

7 years ago

Project for Thijs B.

He's very kind and helpful. Highly recommend!

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Experience

Software Engineer: Finite Element Analysis patient-specific modeling

Aug, 2017 - Feb, 2024

6 years, 6 months

FEops

Aug, 2017 - Feb, 2024

6 years, 6 months

I have worked as a (python) software engineer at FEops for over 5 years, during which I’ve been involved with many projects related to medical image processing, automated analysis, Finite Element Analysis (FEA), 3D mesh generation and data visualization. I was also involved with re-structuring and testing code for obtaining FDA approval for the code to be used as a medical device. The code that I helped develop has effectively been used to treat thousands of cardiac patients around the world.

Aug, 2017 - Feb, 2024

6 years, 6 months

Radiology internship

Nov, 2015 - Feb, 2016

3 months

AMC - Academic Medical Center Amsterdam

Nov, 2015 - Feb, 2016

3 months

During this internship at the AMC hostpital in Amsterdam, I worked together with the radiology and radiotherapy departments where I investigated the formation of MRI artifacts around biliary stents. These artifacts are usually adjacent to pancreatic tumors, obstructing treatment planning for irradiation therapy. Understanding such artifacts allows reconstruction of the MRI images to enable more precise radiation therapy planning.

Nov, 2015 - Feb, 2016

3 months

Education

Universiteit Twente

2010 - 2017

7 years

Master of Science: Biomedical engineer

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Netherlands

2010 - 2017

7 years

Qualifications

Coordinating Radiation Expert

2019

University of Nijmegen

A certificate to prove qualification in radiation expertise (level 3). With this certificate I am qualified to handle radioactive materials and coordinate the use of such materials in laboratories.

2019

Deep Learning Nanodegree

2019

Udacity

A 4 month online course from Udacity covering many aspects of deep neural networks. For this course I built and trained RNN's, CNN's, GAN's, as well as build a NN from scratch using only Numpy. This course also included training in the use of AWS for training neural networks.

2019

Artificial Intelligence Nanodegree

2018

Udacity

A 4 month online program from Udacity covering many artificial intelligence techniques such as tree search methods, heuristics, Bayesian inference, hidden Markov models, etc.

2018

Publications

Quantitative assessment of biliary stent artifacts on MR images: Potential implications for target..

Medical Physics

Biliary stents may cause susceptibility artifacts, gradient‐induced artifacts, and radio frequency (RF) induced artifacts on magnetic resonance images, which can hinder accurate target volume delineation in radiotherapy. In this study, the authors investigated and quantified the magnitude of these artifacts for stents of different materials.

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