Help & Supportcareers@gravityer.com
Postdoctoral researcher Machine vision and deep learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Xidian University
Software Engineer
June 29, 2026 – Present
STAC
December 22, 2025 – Present
This work proposes STAC, a novel framework for weakly supervised defect localization that leverages saliency-guided transformer attention and pixel-level contrastive learning to achieve precise defect maps using only image-level labels.
View ProjectProMSC
September 12, 2025 – Present
This repo contains the official implementation of ProMSC, a novel semi-supervised framework for defect segmentation under limited annotations. It features cross-sample prototype matching and multiscale spatial correlation consistency, achieving state-of-the-art results on multiple industrial defect datasets.
View ProjectAwesome-Machine-Vision-and-Anomaly-Detection
January 27, 2024 – Present
This repo contains state-of-the-art deep learning models for industrial anomaly detection, defect segmentation, detection, and classification, with other industrial machine vision applications.
View Projectdjene-mengistu.github.io
November 20, 2023 – Present
This repo contains CV, research interest, and selected publication lists.
View ProjectUAPS
April 25, 2023 – Present
This repo contains implementation of uncertainty estimation, rectification, and minimization for guiding the pseudo-label learning in semi-supervised defect segmentation setting.
View ProjectsimEps
May 30, 2022 – Present
This repo contains implementation of semi-supervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudo labels
View Projectdseg_models
July 19, 2021 – Present
This repo contains implementation of deep learning-based steel surface defect segmentation models. Extensive experiments on several deep learning frameworks have been presented with various performance analysis and comparison.
View ProjectCultural Fit Analysis
The candidate's projects are exclusively personal and heavily concentrated on academic research in machine vision and anomaly detection. While this demonstrates deep expertise in a niche area, the lack of diverse project types (e.g., team projects, open-source contributions outside research, full-stack development, or general software engineering applications) suggests a potentially narrow cultural fit for a broader 'Software Engineer' role that might require diverse skill sets and collaborative development practices. The single listed professional experience is current and lacks details, making it hard to assess alignment with typical industry roles.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong research-oriented mindset and a focus on developing novel solutions. However, without psychometric test results or interview data, it is difficult to assess soft skills like teamwork, communication, or stress handling. The operational fit for a general 'Software Engineer' role is unclear given the highly specialized research focus.