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Why Your Chinese-Tenured Faculty Resume Won’t Work in US Industry US data scientist hiring managers scan a resume in 7–15 seconds looking for one thing: evidence you can solve business problems with data. A Chinese faculty resume often leads with tenure status, publication counts, and grant amounts—none of which translate to industry value. Worse, the CV-style length and Chinese-specific qualifications (e.g., “Professor of Record,” “National Natural Science Foundation PI”) confuse HR software and recruiters unfamiliar with that system. You need to strip the academic frame and rebuild around what a US data scientist does: clean messy data, build predictive models, deploy to production, and communicate results to non-technical stakeholders. Think of every faculty achievement as raw material you must reframe. Core Rewriting Rules: From Academic to Industry Rule 1: Replace Tenure Rank with a US-Equivalent Data Science Title Do not list “Tenured Associate Professor” unless it is your most recent position at a well-known university (e.g., Peking University, Tsinghua). Instead, use a title that reveals your function: “Senior Data Scientist – Research Computing” or “Lead Data Scientist – Machine Learning Research Lab.” The point is to signal the job function, not the academic rank. Example: Before: “Tenured Associate Professor, School of Computer Science, Fudan University” After: “Senior Data Scientist / Research Lead, Fudan University AI Lab” Rule 2: Translate Every Accomplishment into a Business-Relevant Metric Chinese faculty resumes often say “published 15 papers in top-tier journals” or “secured ¥3M in research funding.” That means nothing to a hiring manager at a fintech startup. Instead, describe what you did with the data and the outcome. Concrete example – before and after: BEFORE (faculty bullet): “Led research project on deep learning for medical image segmentation; published 3 papers in IEEE TMI.” AFTER (industry data scientist bullet): “Built and validated a co

Jackery is jumping on the fridge-battery trend with what it says is the "world's slimmest." FridgeGuard also looks nice; a break from power stations that tend to look more at home at a job site than the kitchen or living room. Measuring just 2.63 inches (67mm) thick, the Jackery FridgeGuard power station is meant to […]

This will be the second market to have an Uber robotaxi service outfitted with Lucid EVs equipped with a self-driving system from Nuro.

Temperatures have climbed up to 45 degrees Fahrenheit above normal, stopping ice from forming in the dead of Antarctic winter.

Introduction Dans le cadre de mon apprentissage des pratiques DevOps modernes, j’ai conçu et implémenté un pipeline CI/CD (Continuous Integration / Continuous Deployment) capable de déployer automatiquement une application web frontend sur deux environnements de production distincts : Vercel et GitHub Pages . Cette mission constitue une application concrète des concepts fondamentaux du DevOps, notamment l’automatisation des processus, la réduction des interventions manuelles et la mise en place d’une chaîne de livraison logicielle fiable et reproductible. Tableau comparatif des plateformes Dans ce projet, le déploiement sur les deux plateformes n’est pas un doublon mais une démarche pédagogique et technique délibérée permettant de tester la flexibilité de l'orchestrateur. Voici comment elles se comparent : Critère GitHub Pages Vercel Hébergement Statique uniquement Statique + SSR + Serverless Domaine gratuit username.github.io projet.vercel.app CI/CD intégré Via GitHub Actions Natif + GitHub Actions Performance Bonne Excellente (Edge Network) Previews PR Non Oui (automatique) Gratuit Oui (illimité) Oui (avec limites) Cas d’usage Portfolios, docs Apps React/Next.js, SaaS ⚠️ Le problème du double déploiement : Laisser Vercel en mode automatique génère un conflit critique avec GitHub Actions. Pour éviter que deux builds s'exécutent en parallèle, j'ai désactivé le déploiement natif de Vercel en ajoutant un fichier vercel.json à la racine contenant "git": { "deploymentEnabled": false } . Le pipeline complet — deploy.yml Voici le code source du fichier de configuration de l'orchestrateur GitHub Actions ( .github/workflows/deploy.yml ). Ce script gère séquentiellement l'installation, le build et la publication vers nos deux cibles : name : CI/CD -- Deploy to Vercel & GitHub Pages # Declenchement : uniquement sur push vers main on : push : branches : - main jobs : build-and-deploy : runs-on : ubuntu-latest permissions : contents : write steps : # Etape 1 : Recuperer le code

Transferring genes across species doesn't just happen in microbes.

Warm weather has fueled a bloom that National Park Service workers are trying to kill using everything from hydrogen peroxide to nanobubbles ahead of July 4 celebrations.

How we live now is defined by unprecedented forces. In this special issue, WIRED and Architectural Digest help you understand what home will look like tomorrow—and beyond.

Legal victories have dampened the Trump admin’s efforts to halt wind and solar power.

Designers are finding sustainable building solves close to home—in ancient practices and cutting-edge innovations alike.

WIRED surveyed readers on their housing costs. The answers paint a stark portrait of unaffordability, climate adaptation, and the death of the homeowner dream.

A new, AI-assisted model of insurance is quietly exploding in disaster-prone areas—and may be coming for FEMA too. Is it the answer to climate change, or a trap?

The global editorial directors of WIRED and Architectural Digest on teaming up to help you understand how we live today, and what comes next.

Devices that monitor seniors for safety are appealing to worried loved ones and underresourced home care agencies.

Probably wants to prevent hallucinations and factual errors from reaching users, and achieve accuracy on par with deterministic systems.

Paul Klein discusses the distributed systems challenges of scaling cloud-hosted browser infra for AI agents. He explains how to manage bursty, stateful multi-tenancy and secure Chromium environments against remote code execution using Firecracker. He also shares how to leverage the Model Context Protocol (MCP) to turn complex websites into accessible agentic tools. By Paul Klein

The deal is supposed to help SpaceX's struggling AI division. The company told IPO investors it sees a $26 trillion addressable market in AI.

Almost three years in, Meta's X competitor is still growing.

The Meta-owned social platform announced a series of new features launching today, including a "Your Algo" tool that lets users control what they see in their feeds